interactive_map
library(tidyverse)
library(leaflet)
library(lubridate)
library(plotly)
library(jpeg)
library(ggpubr)## load sensor data
sensor_data <- read_csv("Sensor Data.csv")
sensor_data_clean <- sensor_data %>%
rename(
time = `Date Time`
) %>%
mutate(time = mdy_hm(time))## load meteorological data
weather_data <- read_csv("Meteorological Data.csv")
weather_data_clean <- weather_data %>%
rename(
time = Date
) %>%
mutate(time = mdy_hm(time))## load traffic data
traffic_data <- read_csv("Lekagul Sensor Data.csv")
traffic_data_clean <- traffic_data %>%
rename(
time = Timestamp
) %>%
mutate(time = ymd_hms(time))map2 <- readJPEG("Lekagul Roadways labeled v2 copy.jpg") ## manually entering in the locations of the factories, which come from "Data Descriptions for Sensor Data.docx", in the Sensor Data file
## first, manually entering in the factory names
factories <- data.frame(c("Roadrunner", "Kasios", "Radiance", "Indigo"))%>%
rename(location_name = c..Roadrunner....Kasios....Radiance....Indigo..)
## then, manually entering in the X coordinates
locationsX <- data.frame(c(89, 90, 109, 120)) %>%
rename(X = c.89..90..109..120.)
## then, manually entering in the Y coordinates
locationsY <- data.frame(c(27, 21, 26, 22)) %>%
rename(Y = c.27..21..26..22.)
## finally, putting the three columns together so that the rows match up
factory_locations <- cbind(factories, locationsX, locationsY) %>%
mutate(location_type = "factory")
## manually entering in the locations of the sensors, which come from the "Data Descriptions for Sensor Data.docx", in the Sensor Data file
## first, manually entering in the name of the sensors
name <- data.frame(c("Sensor 1","Sensor 2","Sensor 3","Sensor 4","Sensor 5","Sensor 6","Sensor 7","Sensor 8","Sensor 9")) %>%
rename(location_name = c..Sensor.1....Sensor.2....Sensor.3....Sensor.4....Sensor.5...)
## next, manually entering in the X locations of each sensor
sensorLocX <- data.frame(c(62, 66, 76, 88, 103, 102, 89, 74, 119)) %>%
rename(X = c.62..66..76..88..103..102..89..74..119.)
## then, manually entering in the Y locations of each sensor
sensorLocY <- data.frame(c(21, 35, 41, 45, 43, 22, 3, 7, 42)) %>%
rename(Y = c.21..35..41..45..43..22..3..7..42.)
## finally, putting the three columns together to form a data frame
sensor_locations <- cbind(name, sensorLocX, sensorLocY) %>%
mutate(location_type = "sensor")
important_locations <-rbind(factory_locations, sensor_locations)ggplot(important_locations, aes(x = X, y = Y, color = location_type)) +
background_image(map2) +
geom_point(size = 2) +
scale_y_continuous(limits = c(0,200)) +
scale_x_continuous(limits = c(0,200))
#, breaks = c(0, 200, by = 10), 1
#, breaks = c(0,200, by = 10), 1# adding the camping X and Y coordinates
traffic_data_coord <- traffic_data_clean%>%
rename(car_id = `car-id`)%>%
rename(gate_name = `gate-name`)%>%
mutate(X = ifelse(gate_name == "camping5", 10,
ifelse(gate_name == "camping0", 50,
ifelse(gate_name== "camping1", 140,
ifelse(gate_name=="camping2", 45,
ifelse(gate_name == "camping3", 46,
ifelse(gate_name == "camping4", 48,
ifelse(gate_name == "camping6",155,
ifelse(gate_name == "camping7", 190,
ifelse(gate_name == "camping8", 195,
ifelse(gate_name == "entrance0", 55,
ifelse(gate_name == "entrance1", 9,
ifelse(gate_name== "entrance2", 192,
ifelse(gate_name=="entrance3", 115,
ifelse(gate_name == "entrance4", 146,
ifelse(gate_name == "gate0", 60,
ifelse(gate_name == "gate1", 55,
ifelse(gate_name== "gate2", 20,
ifelse(gate_name=="gate3", 153,
ifelse(gate_name == "gate4", 170,
ifelse(gate_name == "gate5", 135,
ifelse(gate_name == "gate6", 117,
ifelse(gate_name == "gate7", 96,
ifelse(gate_name == "gate8", 143,
ifelse(gate_name == "general-gate0", 110,
ifelse(gate_name == "general-gate1", 60,
ifelse(gate_name== "general-gate2", 104,
ifelse(gate_name=="general-gate3", 194,
ifelse(gate_name == "general-gate4", 65,
ifelse(gate_name == "general-gate5", 130,
ifelse(gate_name == "general-gate6", 140,
ifelse(gate_name == "general-gate7", 60,
ifelse(gate_name == "ranger-base", 130,
ifelse(gate_name == "ranger-stop0", 90,
ifelse(gate_name== "ranger-stop1", 13,
ifelse(gate_name=="ranger-stop2", 80,
ifelse(gate_name == "ranger-stop3", 152,
ifelse(gate_name == "ranger-stop4", 10,
ifelse(gate_name == "ranger-stop5", 154,
ifelse(gate_name == "ranger-stop6", 125,
ifelse(gate_name == "ranger-stop7", 101, 0)))))))))))))))))))))))))))))))))))))))))%>%
mutate(Y = ifelse(gate_name == "camping5", 75,
ifelse(gate_name == "camping0", 160,
ifelse(gate_name== "camping1", 155,
ifelse(gate_name=="camping2", 140,
ifelse(gate_name == "camping3", 136,
ifelse(gate_name == "camping4", 115,
ifelse(gate_name == "camping6",20,
ifelse(gate_name == "camping7", 50,
ifelse(gate_name == "camping8", 157,
ifelse(gate_name == "entrance0", 196,
ifelse(gate_name == "entrance1", 136,
ifelse(gate_name== "entrance2", 113,
ifelse(gate_name=="entrance3", 26,
ifelse(gate_name == "entrance4", 8,
ifelse(gate_name == "gate0", 170,
ifelse(gate_name == "gate1", 160,
ifelse(gate_name== "gate2", 150,
ifelse(gate_name=="gate3", 143,
ifelse(gate_name == "gate4", 80,
ifelse(gate_name == "gate5", 50,
ifelse(gate_name == "gate6", 45,
ifelse(gate_name == "gate7", 35,
ifelse(gate_name == "gate8", 15,
ifelse(gate_name == "general-gate0", 199,
ifelse(gate_name == "general-gate1", 180,
ifelse(gate_name== "general-gate2", 170,
ifelse(gate_name=="general-gate3", 149,
ifelse(gate_name == "general-gate4", 101,
ifelse(gate_name == "general-gate5", 90,
ifelse(gate_name == "general-gate6", 57,
ifelse(gate_name == "general-gate7", 51,
ifelse(gate_name == "ranger-base", 20,
ifelse(gate_name == "ranger-stop0", 191,
ifelse(gate_name== "ranger-stop1", 182,
ifelse(gate_name=="ranger-stop2", 170,
ifelse(gate_name == "ranger-stop3", 157,
ifelse(gate_name == "ranger-stop4", 102,
ifelse(gate_name == "ranger-stop5", 80,
ifelse(gate_name == "ranger-stop6", 49,
ifelse(gate_name == "ranger-stop7", 46,
0)))))))))))))))))))))))))))))))))))))))))The names of the locations and how many times they are repeated
location_names <- traffic_data_clean%>%
group_by(`gate-name`)%>%
summarize(N = n())Analyzing the key dates
Dates to look at:
4/2/16, monitor 6, Methylosmolene 4/9/16, monitor 6, Methylosmolene 8/1/16, monitor 3, Methylosmolene 8/2/16, monitor 2, Chlorodinine 12/2/16, monitor 6, Methylosmolene 12/23/16, monitor 6, Chlorodinine
2016-04-02
# different colors for different ids not showing up in the legend
data_04_02 <- traffic_data_coord%>%
filter(year(time) == 2016 & month(time)==04 & day(time) == 02)
data_04_02%>%
plot_ly(
x = ~X,
y = ~Y,
#ids = ~car_id,
color = ~factor(car_id),
frame = ~hour(time),
text = ~paste("Location:", gate_name, "\nCar ID:", car_id, "\nCar Type:", `car-type`),
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
)%>%
layout(title = '2016-04-02')## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement lengthanim_graph <- function(my_year, my_month, my_day){
dataname <- traffic_data_coord%>%
filter(year(time) == my_year & month(time)==my_month & day(time) == my_day)
plot <- dataname%>%
plot_ly(
x = ~X,
y = ~Y,
#ids = ~car_id,
color = ~factor(car_id),
frame = ~hour(time),
text = ~paste("Location:", gate_name, "\nCar ID:", car_id, "\nCar Type:", `car-type`),
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
)%>%
layout(title = paste(my_year, "/", my_month, "/", my_day))
}4/2/16, monitor 6, Methylosmolene
4/9/16, monitor 6, Methylosmolene
8/1/16, monitor 3, Methylosmolene
8/2/16, monitor 2, Chlorodinine
12/2/16, monitor 6, Methylosmolene
12/23/16, monitor 6, Chlorodinine
graph_04_02 <- anim_graph(2016, 04, 02)
graph_04_02## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement lengthgraph_04_09 <- anim_graph(2016, 04, 09)
graph_04_09## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement lengthgraph_08_02 <- anim_graph(2016, 08, 02)
graph_08_02graph_12_02 <- anim_graph(2016, 12, 02)
graph_12_02graph_12_23 <- anim_graph(2016, 12, 23)
graph_12_23No records for 08/02, 12/02, 12/23
Tracking car id ’s
“20151112031119-409”
“20155201025245-696”
“20152824032830-251”
“20150416040441-902”
“20152925022919-735”
“20154907044911-419”
“20150204100226-134”
“20160623090611-424”
“20153427103455-30”
# How to add gate name in the hover info
# how to add background image
tracking_cars <- function(id){
p <- traffic_data_coord%>%
filter(car_id == id)%>%
plot_ly(
x = ~X,
y = ~Y,
ids = ~factor(car_id),
frame = ~factor(time),
text = ~paste("Location:", gate_name, "\nCar ID:", car_id, "\nCar Type:", `car-type`),
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
)%>%
layout(title = paste("Car ID:", id))
p
}track_id <- c("20151112031119-409",
"20155201025245-696",
"20152824032830-251",
"20150416040441-902",
"20152925022919-735",
"20154907044911-419",
"20150204100226-134",
"20160623090611-424",
"20153427103455-30")
map(track_id, tracking_cars)## [[1]]
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## [[9]]tracking_cars("20151112031119-409")tracking_cars("20155201025245-696")tracking_cars("20152824032830-251")tracking_cars("20150416040441-902")tracking_cars("20152925022919-735")tracking_cars("20154907044911-419")tracking_cars("20150204100226-134")tracking_cars("20160623090611-424")tracking_cars("20153427103455-30")